Prognostic model and risk factors for hospital mortality in patients with diffuse large B-cell lymphoma associated with coronavirus infection: a single-center cohort study

Author:

Polyakov Yu. Yu.1ORCID,Baryakh E. A.2ORCID,Misyurina E. N.1ORCID,Zhelnova E. I.1ORCID,Yatskov K. V.3ORCID,Makeshova A. B.1ORCID,Mingalimov M. A.1ORCID,Tolstykh T. N.1ORCID,Chudnova T. S.1ORCID,Ivanova D. D.3ORCID,Koneva A. I.3ORCID,Kochneva O. L.3ORCID,Zotina E. N.1ORCID,Gagloeva D. E.1ORCID,Grishina E. Yu.3ORCID,Shimanovskaya L. T.3ORCID,Yakimets V. N.3ORCID

Affiliation:

1. City Clinical Hospital No. 52, Moscow Healthcare Department; I.M. Sechenov First Moscow State Medical University, Ministry of Health of Russia (Sechenov University)

2. City Clinical Hospital No. 52, Moscow Healthcare Department; I.M. Sechenov First Moscow State Medical University, Ministry of Health of Russia (Sechenov University); Russian Medical Academy of Continuing Professional Education, Ministry of Health of Russia; N.I. Pirogov Russian National Research Medical University, Ministry of Health of Russia

3. City Clinical Hospital No. 52, Moscow Healthcare Department

Abstract

Background. Coronavirus disease (COVID-19), caused by SARS-CoV-2, presents new challenges to hematologists, highlighting the vulnerability of patients with hematological malignancies, in particular with diffuse large B-cell lymphoma (DLBCL). Identification of hospital mortality risk factors is necessary for subsequent stratification of patients into risk groups, which will allow further risk-based therapy.Aim. To develop a prognostic model and identify risk factors for hospital mortality in patients with DLBCL associated with COVID-19.Materials and methods. The interim retrospective study included 112 patients with an immunohistochemically confirmed diagnosis of DLBCL, coronavirus infection verified based on polymerase chain reaction (PCR) for SARS-CoV-2, and viral pneumonia associated with COVID-19. To determine the risk factors for hospital mortality, a multivariate (logistic regression) statistical analysis was performed. The study end point was a binary variable - the patient vital status (discharged alive or died).Results and conclusion. Of the 112 patients, 24 died. Due to the limited number of patients compared to the number of predictors and to avoid overfitting, a two-stage approach to constructing a predictive model was used. In univariate analysis, statistically significant during hospitalization were the hematological disease status (complete remission/partial remission, progression/relapse, de novo), positive PCR result, C-reactive protein level >6 mg/L, platelets <100 thousand/pL, hemoglobin <120 g/L, albumin <35 g/L, lactate dehydrogenase >248 U/L, D-dimer >500 ng/mL and the degree of lung tissue damage according to computed tomography >50 % (grade II and above), respiratory failure I degrees and higher. The final model was constructed by minimizing the Akaike information criterion. The final model included a positive PCR result, stage II respiratory failure, hematologic disease status (relapse/progression), and albumin level at the time of hospital admission.

Publisher

Publishing House ABV Press

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